| 小尺度下茶园土壤有效态微量元素空间变异特征及其影响因素分析 |
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| 引用本文： | 夏凡,王永东,郑子成,李廷轩,廖桂堂,刘涛,张锡洲.小尺度下茶园土壤有效态微量元素空间变异特征及其影响因素分析[J].植物营养与肥料学报,2022,28(6):1047-1054. |
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| 作者姓名： | 夏凡 王永东 郑子成 李廷轩 廖桂堂 刘涛 张锡洲 |
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| 作者单位： | 1.四川农业大学资源学院，四川成都 611130 |
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| 基金项目： | 四川省科技支撑项目(2013NZ0044)。 |
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| 摘 要： | 【目的】 开展小尺度下茶园土壤有效态微量元素空间变异及影响因素研究，以指导茶园养分管理和土壤培肥。 【方法】 于2020年，在四川雅安名山区中峰镇面积约2 km2的生态茶园，采用随机布点法布设94个采样点，每个采样点取 0—30 cm 表层土壤，分析土壤pH、有机质以及有效铁、有效锰、有效铜和有效锌4种微量元素含量，用阈值法(平均值±3倍标准差)对分析数据进行异常值处理。采用地统计学与GIS空间分析技术相结合的方法，探讨了小尺度下茶园土壤有效铁、有效锰、有效铜和有效锌4种有效态微量元素的空间异质性，并运用相关性分析和回归分析方法探究其影响因素。 【结果】 研究区土壤有效铁、有效锰、有效铜和有效锌平均含量分别为56.0、28.0、1.2和1.8 mg/kg，变异系数分别为52.59%、89.95%、38.81%和32.90%，属于中等程度变异。有效铁、有效铜和有效锌含量均服从正态分布，而有效锰含量服从对数正态分布。土壤有效铜呈纯块金效应，全局Moran’s I指数为负值；有效铁、有效锰以指数模型为最优拟合模型，有效锌以球面模型为最佳，块基比在36.57%～61.85%，均呈中等空间相关性，全局Moran’s I指数均为正值，且有效铁含量具有极显著空间自相关性。4种土壤有效态微量元素空间分布格局差异较大，有效铁含量呈现南高北低的趋势，有效锰含量随地面坡度的增加呈现降低趋势，有效铜含量斑块状分布明显，有效锌含量则呈现出从研究区两端向中部递减的分布特征。有效铁含量与有机质、pH显著正相关，有效锌含量则与有机质呈极显著正相关，有效锰含量与坡度之间存在显著相关性，有效铜含量则与各因子均无显著相关性。 【结论】 中锋生态茶园土壤有效铁、有效锰、有效铜和有效锌含量总体上均较丰富，均属中等变异强度。土壤有效铜存在空间孤立现象，有效铁、有效锰和有效锌表现为空间聚集特征。茶园土壤有效铁和有效锌含量主要受土壤有机质含量的影响，有效锰含量则主要受坡度的影响。
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| 关 键 词： | 土壤微量元素 茶园 小尺度 空间变异 影响因素 |
| 收稿时间： | 2021-10-27 |
Spatial variability of soil microelements in a small scale tea garden and the influencing factors |
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| XIA Fan,WANG Yong-dong,ZHENG Zi-cheng,LI Ting-xuan,LIAO Gui-tang,LIU Tao,ZHANG Xi-zhou.Spatial variability of soil microelements in a small scale tea garden and the influencing factors[J].Plant Nutrition and Fertilizer Science,2022,28(6):1047-1054. |
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| Authors: | XIA Fan WANG Yong-dong ZHENG Zi-cheng LI Ting-xuan LIAO Gui-tang LIU Tao ZHANG Xi-zhou |
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| Institution: | 1.College of Resources, Sichuan Agricultural University, Chengdu, Sichuan 611130, China |
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| Abstract: | 【Objectives】 Studying the spatial variation of microelements and the influencing factors could improve nutrient management for high tea yield and quality. 【Methods】 There were 94 soil samples (0–30 cm) collected randomly in an ecological tea garden within 2 km2 at Zhongfeng Town, Mingshan District, Ya’an in 2020. The soil pH, organic matter (SOM) and available iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) contents were analyzed. Data obtained were screened by the threshold value method (mean ± three times of standard deviation) before using geostatistics and the GIS spatial analysis method for the spatial variation modelling. Additionally, the correlation of the nutrient contents was regressed with the influencing factors of soil microelements. 【Results】 The average contents of available Fe, Mn, Cu, and Zn in the tea garden soil were 56.0 mg/kg, 28.0 mg/kg, 1.2 mg/kg, and 1.8 mg/kg, with the coefficients of variation of 52.59%, 89.95%, 38.81%, and 32.90%, respectively. These results showed a moderate variation. Available Cu showed a pure nugget effect, and the global Moran’s I index was negative. The optimal interpolation models of available Fe and Mn were exponential, while available Zn were spherical. The nugget effect ranged from 36.57% to 61.85%, showing moderate spatial correlation. The global Moran’s I index of available Fe, Mn, and Zn was positive. Available Fe showed a high (P<0.05) spatial autocorrelation. The spatial distribution of available Fe was higher in the south and lower in the northern region of the research area. In contrast, available Mn exhibited a decrease with the increasing slope. Available Cu and Zn were distributed in patches, with high concentrations along the perimeter and low in the middle of the region. Available Fe was (P<0.05) affected by organic matter and pH. Available Mn was (P<0.05) related to slope only, available Cu content showed no correlation with the influencing factors, and available Zn content was (P<0.05) positively related to organic matter. 【Conclusions】 The soil available Fe, Mn, Cu, and Zn contents in the study area are relatively abundant with a moderate variation. The available Cu shows spatial isolation, while Fe, Mn, and Zn show spatial aggregation. The available Fe and Zn are affected by organic matter, and available Mn is merely related to the slope. No obvious relationship is found between the soil available Cu and the various factors, implying that it can be adopted to reveal the drivers of its spatial distribution from the perspective of farm management. |
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